As existing genetic algorithm, the one described in JP-A-2001-195380 has been known. The existing genetic algorithm will be briefly described with reference to a flow chart in FIG. 8.
In FIG. 8, a step S1 is a step for inputting initial values for respective setting values such as the number of elements in a pattern, the number of patterns in a pattern group, the target value of fitness, etc. A pattern generation group step S2 thereafter is a step for generating a pattern group comprising a plurality of mutually different patterns. A manipulating step S3 thereafter is a step for extracting a predetermined number of patterns from the pattern group and performing manipulation such as crossover on the elements of these patterns to generate new manipulated patterns. A selection step S4 thereafter is a step for selecting the same number of patterns having mutually different degree of adaptation as the extracted patterns from the extracted patterns and manipulated patterns based on the characteristics (degree of adaptation) obtained from these patterns. A substitution step S5 thereafter is a step adding a predetermined number of patterns selected in the selecting step to the pattern group in place of the extracted patterns. A step S6 thereafter is a step for repeating a series of algorithmic process steps comprising the manipulating step, the selection step and the substitution step until the best characteristic values in the preceding pattern group of the step S6 obtained in the algorithmic process steps may fall in the range of a desired value.
In the optimization method of using the genetic algorithm described above, calculations shown by the flow chart of FIG. 8 are repeated but it may possibly stain in a local solution since all the patterns belong to an identical pattern group. Therefore, a number of calculations had to be repeated in order to find an optimized value while avoiding such a local solution. In other words, it involves a subject to take much time until the optimized value is found in a case where a demand level for optimization is increased.